An anomaly detection framework for identifying energy theft and defective meters in smart grids


Yip, Sook Chin and Tan, Wooi Nee and Tan, Chia Kwang and Gan, Ming Tao and Wong, Kok Sheik (2018) An anomaly detection framework for identifying energy theft and defective meters in smart grids. International Journal of Electrical Power & Energy Systems, 101. pp. 189-203. ISSN 0142-0615

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Smart meters are progressively deployed to replace its antiquated predecessor to measure and monitor consumers’ consumption in smart grids. Although smart meters are equipped with encrypted communication and tamper-detection features, they are likely to be exposed to multiple cyber attacks. These meters may be easily compromised to falsify meter readings, which increases the chances and diversifies the types of energy theft. To thwart energy fraud from smart meters, utility providers are identifying anomalous consumption patterns reported to operation centers by leveraging on consumers’ consumption data collected from advanced metering infrastructure. In this paper, we put forward a new anomaly detection framework to evaluate consumers’ energy utilization behavior for identifying the localities of potential energy frauds and faulty meters. Metrics known as the loss factor and error term are introduced to estimate the amount of technical losses and capture the measurement noise, respectively in the distribution lines and transformers. The anomaly detection framework is then enhanced to detect consumers’ malfeasance and faulty meters even when there are intermittent cheating and faulty equipment, improving its robustness. Results from both simulations and test rig show that the proposed framework can successfully locate fraudulent consumers and discover faulty smart meters

Item Type: Article
Uncontrolled Keywords: Anomaly detection, Non-technical losses, Technical losses, Smart grids, AMI, Linear programming
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 08 Nov 2020 13:41
Last Modified: 28 Feb 2023 03:11


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